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Abstract: Variation in the CACNA1C gene has consistently been associated with psychosis in genome wide association studies. We have previously shown in a sample of n=110 healthy subjects that carriers of the CACNA1C rs1006737 risk variant exhibit hippocampal and perigenual anterior cingulate dysfunction (pgACC) during episodic memory recall. Here, we aimed to replicate our results, by testing for the effects of the rs1006737 risk variant in a new large cohort of healthy controls. We furthermore sought to refine these results by identifying the impact of a CACNA1C specific, gene-wide risk score in the absence of clinical pathology. An independent sample of 179 healthy subjects genotyped for rs1006737 underwent functional magnetic resonance imaging (fMRI) while performing an associative episodic memory task and underwent psychological testing similar to the discovery sample. The effect of gene-wide risk scores was analyzed in the combined sample of 289 subjects. We replicated our discovery findings of hippocampal and pgACC dysfunction in carriers of the rs1006737 risk variant. Additionally, we observed diminished activation of the dorsolateral prefrontal cortex, in the replication sample. Our replicated results as well as this new effect were also observable in the combined sample. Moreover, the same system-level phenotypes were significantly associated with the individual gene-based genetic risk score. Our findings suggest that altered hippocampal and frontolimbic function is associated with variants in the CACNA1C gene. Since CACNA1C variants have been associated repeatedly with psychosis at a genome-wide level, and preclinical data provide convergent evidence for the relevance of the CACNA1C gene for hippocampal and frontolimbic plasticity and adaptive regulation of stress, our data suggest a potential pathophysiological mechanism conferred by CACNA1C variants that may mediate risk for symptom dimensions shared among bipolar disorder, major depression, and schizophrenia. [Copyright &y& Elsevier]

Amygdala function is of high interest for cognitive, social and psychiatric neuroscience, emphasizing the need for reliable assessments in humans. Previous work has indicated unsatisfactorily low within-subject reliability of amygdala activation fMRI measures. Based on basic science evidence for strong habituation of amygdala response to repeated stimuli, we investigated whether a quantification of habituation provides additional information beyond the usual estimate of the overall mean activity. We assessed the within-subject reliability of amygdala habituation measures during a facial emotion matching paradigm in 25 healthy subjects. We extracted the amygdala signal decrement across the course of the fMRI run for the two test–retest measurement sessions and compared reliability estimates with previous findings based on mean response amplitude. Retest-reliability of the session-wise amygdala habituation was significantly higher than the evoked amygdala mean amplitude (intraclass correlation coefficients (ICC) = 0.53 vs. 0.16). To test the task-specificity of this finding, we compared the retest-reliability of amygdala habituation across two different tasks. Significant amygdala response decrement was also seen in a cognitive task (n-back working memory) that did not per se activate the amygdala, but was totally unreliable in that context (ICC ~ 0.0), arguing for task-specificity. Together the results show that emotion-dependent amygdala habituation is a robust and considerably more reliable index than the mean amplitude, and provides a robust potential endpoint for within-subject designs including pharmaco-fMRI studies. [ABSTRACT FROM AUTHOR]

Abstract: The investigation of the brain connectome with functional magnetic resonance imaging (fMRI) and graph theory analyses has recently gained much popularity, but little is known about the robustness of these properties, in particular those derived from active fMRI tasks. Here, we studied the test–retest reliability of brain graphs calculated from 26 healthy participants with three established fMRI experiments (n-back working memory, emotional face-matching, resting state) and two parcellation schemes for node definition (AAL atlas, functional atlas proposed by Power et al.). We compared the intra-class correlation coefficients (ICCs) of five different data processing strategies and demonstrated a superior reliability of task-regression methods with condition-specific regressors. The between-task comparison revealed significantly higher ICCs for resting state relative to the active tasks, and a superiority of the n-back task relative to the face-matching task for global and local network properties. While the mean ICCs were typically lower for the active tasks, overall fair to good reliabilities were detected for global and local connectivity properties, and for the n-back task with both atlases, smallworldness. For all three tasks and atlases, low mean ICCs were seen for the local network properties. However, node-specific good reliabilities were detected for node degree in regions known to be critical for the challenged functions (resting-state: default-mode network nodes, n-back: fronto-parietal nodes, face-matching: limbic nodes). Between-atlas comparison demonstrated significantly higher reliabilities for the functional parcellations for global and local network properties. Our findings can inform the choice of processing strategies, brain atlases and outcome properties for fMRI studies using active tasks, graph theory methods, and within-subject designs, in particular future pharmaco-fMRI studies. [Copyright &y& Elsevier]

Abstract: Even more than in cognitive research applications, moving fMRI to the clinic and the drug development process requires the generation of stable and reliable signal changes. The performance characteristics of the fMRI paradigm constrain experimental power and may require different study designs (e.g., crossover vs. parallel groups), yet fMRI reliability characteristics can be strongly dependent on the nature of the fMRI task. The present study investigated both within-subject and group-level reliability of a combined three-task fMRI battery targeting three systems of wide applicability in clinical and cognitive neuroscience: an emotional (face matching), a motivational (monetary reward anticipation) and a cognitive (n-back working memory) task. A group of 25 young, healthy volunteers were scanned twice on a 3T MRI scanner with a mean test–retest interval of 14.6days. FMRI reliability was quantified using the intraclass correlation coefficient (ICC) applied at three different levels ranging from a global to a localized and fine spatial scale: (1) reliability of group-level activation maps over the whole brain and within targeted regions of interest (ROIs); (2) within-subject reliability of ROI-mean amplitudes and (3) within-subject reliability of individual voxels in the target ROIs. Results showed robust evoked activation of all three tasks in their respective target regions (emotional task=amygdala; motivational task=ventral striatum; cognitive task=right dorsolateral prefrontal cortex and parietal cortices) with high effect sizes (ES) of ROI-mean summary values (ES=1.11–1.44 for the faces task, 0.96–1.43 for the reward task, 0.83–2.58 for the n-back task). Reliability of group level activation was excellent for all three tasks with ICCs of 0.89–0.98 at the whole brain level and 0.66–0.97 within target ROIs. Within-subject reliability of ROI-mean amplitudes across sessions was fair to good for the reward task (ICCs=0.56–0.62) and, dependent on the particular ROI, also fair-to-good for the n-back task (ICCs=0.44–0.57) but lower for the faces task (ICC=−0.02–0.16). In conclusion, all three tasks are well suited to between-subject designs, including imaging genetics. When specific recommendations are followed, the n-back and reward task are also suited for within-subject designs, including pharmaco-fMRI. The present study provides task-specific fMRI reliability performance measures that will inform the optimal use, powering and design of fMRI studies using comparable tasks. [Copyright &y& Elsevier]

Abstract: The drug metabolizing cytochrome P450 2D6 enzyme (CYP2D6) is highly expressed in brain and potentially involved in neurotransmitter biotransformation. Here, we report the effect of the CYP2D6 genotype on brain activation during a working memory and an emotional face matching task measured with fMRI. Subjects were taken from an ongoing large scale multicenter imaging genetic study. CYP2D6 genotyping of the alleles *2, *3, *4, *5, *6, *9, *10, *17, *35, *41 and the duplication was performed in N =114 healthy drug free individuals. All individuals had completed two tasks in functional brain imaging: an n-back working memory task and an implicit emotional face matching task. Contrast images were analyzed in second-level random effects models with CYP2D6 enzyme activity levels as regressor of interest and age, sex and scanning site as covariates. In the working memory task, a significant effect of CYP2D6 genotype was found in the fusiform gyrus and the precuneus. In the emotional face matching task, an effect was detected in the cuneus. No significant activation results were found in the thalamus. A conjunction analysis confirmed a significant joint effect of the CYP2D6 association in both regions. In both tasks activation increased with increasing CYP2D6 activity. In conclusion, we confirmed a central nervous system effect of CYP2D6 activity in a large independent sample using a different imaging modality, and provide evidence that basic cognitive processes related to such as alertness may be impacted. [Copyright &y& Elsevier]

Abstract: Characterizing the brain connectome using neuroimaging data and measures derived from graph theory emerged as a new approach that has been applied to brain maturation, cognitive function and neuropsychiatric disorders. For a broad application of this method especially for clinical populations and longitudinal studies, the reliability of this approach and its robustness to confounding factors need to be explored. Here we investigated test–retest reliability of graph metrics of functional networks derived from functional magnetic resonance imaging (fMRI) recorded in 33 healthy subjects during rest. We constructed undirected networks based on the Anatomic-Automatic-Labeling (AAL) atlas template and calculated several commonly used measures from the field of graph theory, focusing on the influence of different strategies for confound correction. For each subject, method and session we computed the following graph metrics: clustering coefficient, characteristic path length, local and global efficiency, assortativity, modularity, hierarchy and the small-worldness scalar. Reliability of each graph metric was assessed using the intraclass correlation coefficient (ICC). Overall ICCs ranged from low to high (0 to 0.763) depending on the method and metric. Methodologically, the use of a broader frequency band (0.008–0.15Hz) yielded highest reliability indices (mean ICC=0.484), followed by the use of global regression (mean ICC=0.399). In general, the second order metrics (small-worldness, hierarchy, assortativity) studied here, tended to be more robust than first order metrics. In conclusion, our study provides methodological recommendations which allow the computation of sufficiently robust markers of network organization using graph metrics derived from fMRI data at rest. [Copyright &y& Elsevier]